// head_to_head
Mistral Large 2407 vs SpaceXAI: Grok 4.5
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Mistral Large 2407 and SpaceXAI: Grok 4.5 are priced within ~10% of each other.
Mistral Large 2407 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
mistralai
Mistral Large 2407
- Blended / 1M
- $3.00
- Context
- 131K
- Released
- Nov 19, 2024
- Overall score
- Not evaluated
x-ai
SpaceXAI: Grok 4.5
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Jul 8, 2026
- Overall score
- 75.8
Specs and pricing
| Metric | Mistral Large 2407 | SpaceXAI: Grok 4.5 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 75.8 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0720 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $3.00 | $3.00 |
| Input price / 1M | $2.00 | $2.00 |
| Output price / 1M | $6.00 | $6.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200win | $0.300 |
| Context window | 131K | 500Kwin |
| Max output tokens | 105K | 450Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Mistral Large 2407 on top, SpaceXAI: Grok 4.5 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Mistral Large 2407 | SpaceXAI: Grok 4.5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $830.40/mo | $837.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,240/mo | $1,280/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,072/mo | $1,128/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,360/mo | $2,365/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4,700/mo | $4,750/mo |
Which should you pick?
You need to fit large documents in one call
SpaceXAI: Grok 4.5
Wider context window — 500K against 131K.
Mistral Large 2407 vs SpaceXAI: Grok 4.5 FAQ
Which is better, Mistral Large 2407 or SpaceXAI: Grok 4.5?
Mistral Large 2407 and SpaceXAI: Grok 4.5 are priced within ~10% of each other. Mistral Large 2407 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Mistral Large 2407 cheaper than SpaceXAI: Grok 4.5?
They cost about the same. Both land near $3.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Mistral Large 2407 or SpaceXAI: Grok 4.5 have a bigger context window?
SpaceXAI: Grok 4.5 has the larger context window: 131K for Mistral Large 2407 against 500K for SpaceXAI: Grok 4.5. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Mistral Large 2407 and SpaceXAI: Grok 4.5 support prompt caching?
Both publish a cached-input rate: $0.200 per million for Mistral Large 2407 and $0.300 for SpaceXAI: Grok 4.5, against full input rates of $2.00 and $2.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.